Mastering BigQuery Monitoring Dashboard: 8+ Metrics and Strategies

When it comes to managing resources on Google BigQuery, monitoring is an essential aspect. Monitoring allows users to keep track of how their resources are being utilized, identify any performance issues, and make informed decisions about resource allocation.

In this article, we will explore the benefits of monitoring BigQuery resources and how BigQuery’s Monitoring Dashboard can help. We will also be diving into the specifics of BigQuery Metrics, what they are, and how they work.

What is BigQuery?

BigQuery is a powerful data warehousing solution that allows businesses to store and analyze massive amounts of data. As with any resource that is heavily utilized, it is important to monitor BigQuery resources to ensure optimal performance and cost efficiency.

• Google BigQuery is a cloud-based data warehouse that enables scalable analysis of large datasets. It uses massively parallel processing to run queries on terabytes or petabytes of data and return results in seconds.
• As a fully-managed service, BigQuery handles infrastructure administration and maintenance, allowing you to focus on analyzing data. It also provides built-in replications for high availability and performance.
• With its serverless architecture and minimal upkeep requirements, BigQuery makes it easy and affordable to derive insights from huge datasets
• BigQuery integrates smoothly with other GCP services, such as Google Dataflow and Google Analytics, and is an entirely managed service that requires minimal maintenance and administration.

For a comprehensive understanding of your BigQuery expenditures, check out our BigQuery pricing calculator.

Importance of Monitoring BigQuery Resources

By monitoring BigQuery resources, users can gain insights into query latency, query throughput, and other key metrics that are crucial for optimizing system performance. This information can be used to identify trends, predict future resource needs, and improve query performance. Additionally, monitoring can help detect potential security breaches, such as unauthorized access or data leakage.

Monitoring Metrics vs Cloud Logging

When it comes to monitoring and troubleshooting issues in BigQuery, there are two main ways of gathering data: metrics and logs.

What are BigQuery Monitoring Metrics?
Metrics provide a high-level view of resource utilization, giving you information about how your BigQuery resources are performing over time. Metrics can include information about query performance, data size, job counts, and more. These metrics can be used to identify trends and performance issues, allowing you to optimize your BigQuery usage and reduce costs.

What is Cloud Logging in GCP?
Logs provide a more detailed view of what’s happening in your BigQuery environment. They capture events, errors, and other important information that can help you diagnose and resolve issues with specific jobs, queries, or datasets. Logs can provide more granular information than metrics, but they can also generate a lot of data, so it’s important to use them strategically.

• Google’s Cloud Logging feature enables you to maintain observability by having express, non-tampering logs that describe all the activity within your Google Cloud environment.
• Having the ability to store, interact, view and analyze the logs helps you in gaining valuable insights and supplements you in making better decisions.
• Users can visit our guide on Monitoring effectively with Cloud Logging in GCP for more information about logs

Using BigQuery Monitoring Metrics and Custom Dashboards

BigQuery Metrics are collected by the BigQuery service and stored in Cloud Monitoring. You can view these metrics using the BigQuery Dashboard, which provides a real-time view of your BigQuery environment. The dashboard allows you to visualize these metrics in a variety of ways, including line charts, bar charts, and tables.

What is a BigQuery Dashboard?

A BigQuery Dashboard is a customizable visual interface that displays key metrics and information about your BigQuery environment. The dashboard allows you to monitor the health and performance of your BigQuery resources in real-time, and provides insights into the usage, performance, and cost of your queries.

The dashboard is fully customizable, so you can choose which metrics to display and how to visualize them. You can also set up alerts to notify you when certain thresholds are reached, such as when a query exceeds a certain latency or cost threshold.

Requirements for Accessing the Dashboard

Before accessing the BigQuery Metrics Dashboard, users must have an existing GCP account with authorized access to the BigQuery service. Additionally, users must have already created at least one dataset in BigQuery.

Step-by-Step Guide to Accessing the BigQuery Dashboard

To access the BigQuery Metrics Dashboard, follow these steps:

1. View BigQuery Monitoring Dashboard

• Go to the Google Cloud Console Monitoring page
• Select Dashboards > BigQuery to view the BigQuery Monitoring dashboard
  
  
• The dashboard shows tables, events, and incident reports as well as project and dataset metrics charts

2. Adding and Viewing BigQuery Metrics

• Go to the Google Cloud Console Monitoring page
• Select Metrics Explorer, and choose the metric you want to view.
  
  
• From the top right drop-down menu, users can change the type of chart; on the left information panel, users can employ various filters, and make specific, additional changes.
• To add a metric, click the add another metric button and fill in the required details.

Metrics Table

The BigQuery Metrics Table is a useful tool for understanding and monitoring the health of your BigQuery resources. By utilizing the Resource Type, Name, Units, and Description categories, users can gain insight into how their BigQuery environment is performing, allowing them to identify areas that may require optimization or scaling.

Furthermore, users can use this table to create custom metrics that are tailored to their specific needs. By examining the available metrics and determining which ones are most relevant to their use case, users can create a custom dashboard that provides real-time insights into their BigQuery performance.

• To utilize this table effectively, users should first familiarize themselves with the Resource Type, Name, Units, and Description categories.
• Once they understand what each metric represents, they can begin to create their custom metrics based on their specific use case.
• This may involve selecting specific metrics that are most relevant to their environment or creating new metrics that are not currently available in the table.

Benefits and Advantages of Monitoring BigQuery Resources

Monitoring BigQuery resources through the dashboard has several benefits that can greatly improve the efficiency and effectiveness of using this powerful cloud-based data warehousing solution:

• Improved visibility and control – Gain real-time visibility into your BigQuery resources. The dashboard delivers actionable insights to monitor performance, spot bottlenecks, and optimize costs. Make informed decisions to allocate resources efficiently
• Real-time insights into BigQuery performance and scalability – The dashboard provides a window into BigQuery’s performance, scalability, and utilization. Monitor query speeds, scan rates, and other metrics in real-time to enhance performance. Track resource usage and make adjustments to boost ROI. Scale resources up or down based on demand
• Enhanced troubleshooting and issue resolution – Troubleshoot issues and resolve problems quickly. Detailed logs and metrics highlight query errors, resource problems, and other obstacles. Minimize downtime by taking fast corrective action armed with insights from the dashboard
• Helpful cross platform integrations – Unlock the value in your BigQuery data through powerful integrations. Connect to data visualization and BI tools like Tableau and Looker with just a few clicks. Turn your data into interactive reports, dashboards, and visualizations—without transferring or exporting data. Focus on insights instead of connectivity and optimization.
• GCP’s newest update, BigQuery Studio (designed for big data management in organizations using AI, ML, and LLM) is an all-in-one analytics dashboard that streamlines end-to-end analytics workflows, from data ingestion and transformation to sophisticated predictive analysis.

Conclusion

By leveraging the benefits of the BigQuery monitoring dashboard, users can gain better visibility and control over their BigQery usage and resources, leading to improved performance and scalability.

However, monitoring resources is just one aspect of cloud cost optimization. To fully optimize cloud costs, users should implement a FinOps (Financial Operations) approach, which focuses on optimizing cloud usage and reducing expenses. A FinOps approach involves establishing a culture of accountability and cost awareness, implementing cost optimization best practices, and leveraging cost optimization tools like Economize, our cloud cost optimization platform.

With Economize, users can gain deeper insights into their cloud usage and expenses, identify cost optimization opportunities, and automate cost optimization actions to reduce expenses and improve cloud cost management. To learn more about how Economize can help you optimize your cloud costs and achieve better financial operations, click here for a demo.